#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(dendextend)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
suppressWarnings(suppressMessages(library(WGCNA)))
library(expss)
library(polycor)
library(foreach) ; library(doParallel)
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}
Load preprocessed dataset (preprocessing code in 20_02_21_data_preprocessing.Rmd) and clustering (pipeline in 20_02_24_WGCNA.Rmd)
# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame
# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID'=as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal'=1)
# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]
# Clusterings
clusterings = read_csv('./../Data/clusters.csv')
# Update DE_info with SFARI and Neuronal information
genes_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
left_join(GO_neuronal, by='ID') %>% left_join(clusterings, by='ID') %>%
mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`),
significant=padj<0.05 & !is.na(padj))
rm(DE_info, GO_annotations, clusterings)
print(paste0('Dynamic Tree leaves ', sum(genes_info$DynamicTree=='gray'), ' genes without cluster (',
round(mean(genes_info$DynamicTree=='gray')*100), '%)'))
## [1] "Dynamic Tree leaves 433 genes without cluster (3%)"
print(paste0('Dynamic Hybrid leaves ', sum(genes_info$DynamicHybrid=='gray'), ' genes without cluster (',
round(mean(genes_info$DynamicHybrid=='gray')*100,2), '%)'))
## [1] "Dynamic Hybrid leaves 229 genes without cluster (1.42%)"
Dynamic Tree leaves more genes without a cluster, but in previous experiments it returned cleaner results, so I’m going to see which genes are lost to see how big the damage is.
When studying all the brian regions together, there seemed to be a relation between DE and module membership, being DE a more restrictive condition than being assigned to a cluster. Here it’s not that easy to distinguish given the small amount of DE genes and genes left without a cluster, but they could still be related since genes without a cluser have a very small PC2 and DE genes very high absolute PC2
pca = datExpr %>% prcomp
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(genes_info, by='ID') %>% mutate('hasCluster'=DynamicTree!='gray',
'hasSFARIScore'=`gene-score`!='None') %>%
apply_labels(`gene-score`='SFARI Gene score', DynamicTree = 'Dynamic Tree Algorithm',
significant = 'Differentially Expressed', hasCluster = 'Belongs to a Module',
hasSFARIScore = 'Has a SFARI Score', syndromic = 'Has syndromic tag')
p1 = plot_data %>% ggplot(aes(PC1, PC2, color=hasCluster)) + geom_point(alpha=1-0.9*plot_data$hasCluster) +
theme_minimal() + ggtitle('Genes are assigned to a cluster') + theme(legend.position='bottom')
p2 = plot_data %>% ggplot(aes(PC1, PC2, color=significant)) + geom_point(alpha=plot_data$significant+0.1) +
theme_minimal() + ggtitle('Genes found to be DE') + theme(legend.position='bottom')
grid.arrange(p1, p2, nrow=1)
rm(pca, p1, p2)
Most of the genes that don’t have a cluster are not differentially expressed, but almost all genes are not differentially expressed, so this isn’t that remarkable
cat(paste0(round(100*sum(!plot_data$hasCluster & !plot_data$significant)/sum(!plot_data$hasCluster)),
'% of the genes that don\'t have a cluster are not differentially expressed\n'))
## 99% of the genes that don't have a cluster are not differentially expressed
cro(plot_data$significant, list(plot_data$hasCluster, total()))
|  Belongs to a Module |  #Total | |||
|---|---|---|---|---|
| Â FALSEÂ | Â TRUEÂ | Â | ||
|  Differentially Expressed | ||||
| Â Â Â FALSEÂ | 430 | 15700 | Â | 16130 |
| Â Â Â TRUEÂ | 3 | 21 | Â | 24 |
|    #Total cases | 433 | 15721 |  | 16154 |
Most of the genes with a SFARI score are assigned to a cluster
cat(paste0(sum(plot_data$hasSFARIScore & !plot_data$hasCluster), ' of the SFARI genes (~',
round(100*sum(plot_data$hasSFARIScore & !plot_data$hasCluster)/sum(plot_data$hasSFARIScore)),
'%) are not assigned to any cluster\n'))
## 15 of the SFARI genes (~2%) are not assigned to any cluster
cro(plot_data$hasSFARIScore, list(plot_data$hasCluster, total()))
|  Belongs to a Module |  #Total | |||
|---|---|---|---|---|
| Â FALSEÂ | Â TRUEÂ | Â | ||
|  Has a SFARI Score | ||||
| Â Â Â FALSEÂ | 418 | 14835 | Â | 15253 |
| Â Â Â TRUEÂ | 15 | 886 | Â | 901 |
|    #Total cases | 433 | 15721 |  | 16154 |
The main ndifference between algorithms is that Dynamic Hybrid clusters outlier genes and Dynamic Tree leaves them out, so Dynamic Tree would give me a ‘cleaner’ group of genes to work with, without losing many SFARI genes, but Dynamic Hybrid has less and more balanced clusters
I think both options could be feasible, but I’m going to use the Dynamic Hybrid algorithm to keep more genes
Since Dynamic Hybrid returned so many modules, I’m going to use the smallest of the merged modules, DynamicHybridMergedSmall
clustering_selected = 'DynamicHybridMergedSmall'
genes_info$Module = genes_info[,clustering_selected]
*The colour of the modules is the arbitrary one assigned during the WGCNA algorithm, where the gray cluster actually represents all the genes that were left without a cluster (so it’s not actually a cluster).
cat(paste0('The Dynamic Hybrid algorithm created ', length(unique(genes_info$Module))-1, ' modules and leaves ',
sum(genes_info$Module=='gray'), ' genes without a module.\n'))
## The Dynamic Hybrid algorithm created 149 modules and leaves 229 genes without a module.
table(genes_info$Module)
##
## #00A6FF #00A8FF #00AAFE #00ABFC #00ADFA #00AFF8 #00B1F5 #00B2F3 #00B4F0
## 105 14 33 66 111 78 174 558 33
## #00B5ED #00B6EA #00B7E7 #00B811 #00B825 #00B931 #00B9E4 #00BA3C #00BAE1
## 21 75 17 24 15 248 28 30 28
## #00BB45 #00BB4D #00BBDA #00BBDD #00BC54 #00BCD6 #00BD5B #00BD62 #00BDD2
## 80 224 66 45 16 25 20 19 42
## #00BE68 #00BE6E #00BECA #00BECE #00BF74 #00BF7A #00BF80 #00BFC2 #00BFC6
## 13 14 27 113 24 974 16 16 378
## #00C085 #00C08A #00C08F #00C095 #00C0B1 #00C0B5 #00C0BA #00C0BE #00C199
## 690 47 55 17 27 237 26 81 25
## #00C19E #00C1A3 #00C1A8 #00C1AC #17A3FF #21B700 #35B600 #39A1FF #43B500
## 19 16 189 12 45 84 27 22 107
## #4D9FFF #4EB400 #58B300 #5C9DFF #61B200 #69B100 #6A9AFF #70B000 #7598FF
## 69 28 172 37 42 162 213 222 79
## #77AF00 #7DAE00 #8095FF #83AD00 #8993FF #89AC00 #8EAB00 #9290FF #94A900
## 39 149 257 59 38 19 35 37 66
## #98A800 #9A8EFF #9DA700 #A28BFF #A2A600 #A6A400 #AA88FF #ABA300 #AFA200
## 14 77 60 222 39 34 56 24 1418
## #B086FF #B3A000 #B783FF #B79F00 #BA9E00 #BD81FF #BE9C00 #C19B00 #C37EFF
## 107 69 38 84 44 39 25 54 69
## #C59900 #C87BFF #C89800 #CB9600 #CD79FF #CF9500 #D277FF #D29300 #D59100
## 37 32 218 27 23 19 26 14 27
## #D774FD #D79000 #DA8E00 #DB72FB #DD8D00 #DF70F9 #E08B00 #E28900 #E36EF6
## 45 102 315 715 298 105 223 208 73
## #E48800 #E66CF3 #E7861A #E9842A #EA6AF1 #EB8236 #ED68EE #ED8140 #EF7F49
## 313 31 28 57 42 30 42 117 40
## #F067EB #F17D51 #F265E7 #F37B59 #F464E4 #F57A60 #F67866 #F763E1 #F862DD
## 18 23 52 38 23 229 17 97 35
## #F8766D #F97573 #FA62D9 #FB7379 #FC61D5 #FC717F #FD61D1 #FD7085 #FE61CD
## 138 221 25 38 47 190 70 66 20
## #FE6E8A #FF61C1 #FF61C5 #FF61C9 #FF62BC #FF63B8 #FF64B3 #FF65AE #FF66A9
## 32 13 38 50 136 24 86 15 853
## #FF67A5 #FF68A0 #FF699A #FF6B95 #FF6C90 gray
## 22 44 71 255 40 229
plot_data = table(genes_info$Module) %>% data.frame %>% arrange(desc(Freq))
ggplotly(plot_data %>% ggplot(aes(x=reorder(Var1, -Freq), y=Freq)) + geom_bar(stat='identity', fill=plot_data$Var1) +
ggtitle('Module size') + ylab('Number of genes') + xlab('Module') + theme_minimal() +
theme(axis.text.x = element_text(angle = 90)))
In the WGCNA documentation they use Pearson correlation to calculate correlations, I think all of their variables were continuous. Since I have categorical variables I’m going to use the hetcor function, that calculates Pearson, polyserial or polychoric correlations depending on the type of variables involved.
I’m not sure how the corPvalueStudent function calculates the p-values and I cannot find any documentation…
Compared correlations using Pearson correlation and with hetcor and they are very similar, but a bit more extreme with hetcor. The same thing happens with the p-values.
datTraits = datMeta %>% dplyr::select(Diagnosis, Sex, Age, PMI, RNAExtractionBatch) %>%
rename('ExtractionBatch' = RNAExtractionBatch)
# Recalculate MEs with color labels
ME_object = datExpr %>% t %>% moduleEigengenes(colors = genes_info$Module)
MEs = orderMEs(ME_object$eigengenes)
# Calculate correlation between eigengenes and the traits and their p-values
moduleTraitCor = MEs %>% apply(2, function(x) hetcor(x, datTraits)$correlations[1,-1]) %>% t
rownames(moduleTraitCor) = colnames(MEs)
colnames(moduleTraitCor) = colnames(datTraits)
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nrow(datExpr))
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
# In case there are any NAs
if(sum(!complete.cases(moduleTraitCor))>0){
print(paste0(sum(is.na(moduleTraitCor)),' correlation(s) could not be calculated'))
}
## [1] "2 correlation(s) could not be calculated"
rm(ME_object)
Note: The correlation between Module #E08B00 and Diagonsis is the one that cannot be calculated, weirdly enough, the thing that causes the error is that the initial correlation is too high, so it would be a very bad thing to lose this module because of this numerical error. I’m going to fill in its value using the polyserial function, which doesn’t give exactly the same results as the hetcor() function, but it’s quite similar.
# Calculate the correlation tha failed with hetcor()
moduleTraitCor['ME#E08B00','Diagnosis'] = polyserial(MEs[,'ME#E08B00'], datTraits$Diagnosis)
## Warning in polyserial(MEs[, "ME#E08B00"], datTraits$Diagnosis): initial
## correlation inadmissible, 1.07478833176392, set to 0.9999
Modules have very strong correlations with Diagnosis with really small p-values and not much relation with anything else. Perhaps a little with PMI and Brain Region.
They gray ‘module’ no longer has one of the lowest correlations with Diagnosis
# Sort moduleTraitCor by Diagnosis
moduleTraitCor = moduleTraitCor[order(moduleTraitCor[,1], decreasing=TRUE),]
moduleTraitPvalue = moduleTraitPvalue[order(moduleTraitCor[,1], decreasing=TRUE),]
# Filter some modules so the heatmap is easier to plot
# moduleTraitCor = moduleTraitCor[abs(moduleTraitCor[,1])>0.3,]
# moduleTraitPvalue = moduleTraitPvalue[abs(moduleTraitCor[,1])>0.3,]
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = moduleTraitCor, xLabels = names(datTraits), yLabels = gsub('ME','',rownames(moduleTraitCor)),
yColorWidth=0, colors = brewer.pal(11,'PiYG'), bg.lab.y = gsub('ME','',rownames(moduleTraitCor)),
textMatrix = textMatrix, setStdMargins = FALSE, cex.text = 0.8, cex.lab.y = 0.75, zlim = c(-1,1),
main = paste('Module-Trait relationships'))
diagnosis_cor = data.frame('Module' = gsub('ME','',rownames(moduleTraitCor)),
'MTcor' = moduleTraitCor[,'Diagnosis'],
'MTpval' = moduleTraitPvalue[,'Diagnosis'])
genes_info = genes_info %>% left_join(diagnosis_cor, by='Module')
rm(moduleTraitCor, moduleTraitPvalue, textMatrix, diagnosis_cor)
It’s harder to see with so little DE genes, but this plot still shows that Modules with a high Module-Diagnosis (absolute) correlation have a higher content of differentially expressed genes
plot_data = genes_info %>% group_by(Module, MTcor) %>% summarise(p = 100*mean(significant))
plot_data %>% ggplot(aes(MTcor, p)) + geom_hline(yintercept=mean(plot_data$p), color='gray', linetype='dotted') +
geom_point(color=plot_data$Module, aes(id=Module)) + theme_minimal() +
xlab('Modules ordered by Module-Diagnosis correlation') + ylab('Percentage of differentially expressed genes')
Gene significance: is the value between the correlation between the gene and the trait we are interested in. A positive gene significance means the gene is overexpressed and a negative value means its underexpressed. (The term ‘significance’ is not very acurate because it’s not actually measuring statistical significance, it’s just a correlation, but that’s how they call it in WGCNA…)
Module Membership is the correlation of the module’s eigengene and the expression profile of a gene. The higher the Module Membership, the more similar the gene is to the genes that constitute the module. (I won’t use this measure yet)
# It's more efficient to iterate the correlations one by one, otherwise it calculates correlations between the eigengenes and also between the genes, which we don't need
# Check if MM information already exists and if not, calculate it
if(file.exists(paste0('./../Data/dataset_', clustering_selected, '.csv'))){
dataset = read.csv(paste0('./../Data/dataset_', clustering_selected, '.csv'))
dataset$Module = dataset[,clustering_selected]
} else {
############# 1. Calculate Gene Significance
GS_info = data.frame('ID' = rownames(datExpr),
'GS' = datExpr %>% apply(1, function(x) hetcor(x, datMeta$Diagnosis)$correlations[1,2])) %>%
mutate('GSpval' = corPvalueStudent(GS, ncol(datExpr)))
############# 2. Calculate Module Membership
#setup parallel backend to use many processors
cores = detectCores()
cl = makeCluster(cores-1)
registerDoParallel(cl)
# Create matrix with MM by gene
MM = foreach(i=1:nrow(datExpr), .combine=rbind) %dopar% {
library(polycor)
tempMatrix = apply(MEs, 2, function(x) hetcor(as.numeric(datExpr[i,]), x)$correlations[1,2])
tempMatrix
}
# Stop clusters
stopCluster(cl)
rownames(MM) = rownames(datExpr)
colnames(MM) = paste0('MM',gsub('ME','',colnames(MEs)))
# Calculate p-values
MMpval = MM %>% corPvalueStudent(ncol(datExpr)) %>% as.data.frame
colnames(MMpval) = paste0('MMpval', gsub('ME','',colnames(MEs)))
MM = MM %>% as.data.frame %>% mutate(ID = rownames(.))
MMpval = MMpval %>% as.data.frame %>% mutate(ID = rownames(.))
# Join and save results
dataset = genes_info %>% dplyr::select(ID, `gene-score`, clustering_selected, MTcor, MTpval) %>%
left_join(GS_info, by='ID') %>%
left_join(MM, by='ID') %>%
left_join(MMpval, by='ID')
write.csv(dataset, file = paste0('./../Data/dataset_', clustering_selected, '.csv'), row.names = FALSE)
rm(cores, cl)
}
GS_missing = dataset$ID[is.na(dataset$GS)] %>% as.character
if(length(GS_missing)>0){
print(paste0(length(GS_missing),' correlations between genes and Diagnosis could not be calculated, ',
'calculating them with the polyserial function'))
for(g in GS_missing){
dataset$GS[dataset$ID == g] = polyserial(as.numeric(datExpr[g,]), datMeta$Diagnosis)
}
}
## [1] "36 correlations between genes and Diagnosis could not be calculated, calculating them with the polyserial function"
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00086601586701, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, 1.01005534381103, set to 0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.05225322768285, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, 1.00022572924371, set to 0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, 1.00057281088148, set to 0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00517501596365, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, 1.06171755385438, set to 0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.01212301373417, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00633029247751, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, 1.01472473316713, set to 0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, 1.0190691339179, set to 0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00130194444989, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.02454488167405, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00736735929184, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00631216725183, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00150362151356, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.01289562715944, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.01089055195498, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00640240468909, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, 1.00446948637747, set to 0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, 1.01029028386243, set to 0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.07769738434974, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00401974279988, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, 1.01591017247702, set to 0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00556806651229, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.03825753999499, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00814873439015, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.03097451577053, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.01283411453025, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.0954076788885, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.06630750224835, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.0414268492739, set to -0.9999
## Warning in polyserial(as.numeric(datExpr[g, ]), datMeta$Diagnosis): initial
## correlation inadmissible, -1.00676966148363, set to -0.9999
rm(GS_missing)
Gene significance and Log Fold Chance are two different ways to measure the same thing, so there should be a concordance between them
Log Fold Change seems to have a wider range of values and Gene Significance values seem more uniformly distributed, but they do agree with each other
plot_data = dataset %>% dplyr::select(ID, MTcor, GS) %>% left_join(genes_info %>% dplyr::select(ID, gene.score), by='ID') %>%
left_join(genes_info %>% dplyr::select(ID, baseMean, log2FoldChange, significant, Module), by='ID') %>%
left_join(data.frame(MTcor=unique(dataset$MTcor)) %>% arrange(by=MTcor) %>%
mutate(order=1:length(unique(dataset$MTcor))), by='MTcor')
ggplotly(plot_data %>% ggplot(aes(GS, log2FoldChange)) + geom_point(color=plot_data$Module, alpha=0.5, aes(ID=Module)) +
geom_smooth(color='gray') + theme_minimal() + xlab('Gene Significance') +
ggtitle(paste0('Correlation = ', round(cor(plot_data$log2FoldChange, plot_data$GS)[1], 4))))
In general, modules with the highest Module-Diagnosis correlation should have genes with high Gene Significance
Note: For the Module-Diagnosis plots, if you do boxplots, you lose the exact module-diagnosis correlation and you only keep the order, so I decided to compensate this downside with a second plot, where each point is plotted individually using their module’s Module-Diagnosis correlation as the x axis. I think the boxplot plot is easier to understand but the second plot contains more information, so I don’t know which one is better.
plot_data = plot_data %>% arrange(order)
ggplotly(plot_data %>% ggplot(aes(order, GS, group=order)) + geom_hline(yintercept=0, color='gray', linetype='dotted') +
geom_boxplot(fill=unique(plot_data$Module)) + theme_minimal() +
xlab('Modules ordered by Module-Diagnosis correlation') + ylab('Gene Significance'))
plot_data %>% ggplot(aes(MTcor, GS)) + geom_hline(yintercept=0, color='gray', linetype='dotted') +
geom_point(color=plot_data$Module, alpha=0.1, aes(id=ID)) + geom_smooth(color='gray', alpha=0.3) +
theme_minimal() + xlab('Module-Diagnosis correlation') + ylab('Gene Significance') +
ggtitle(paste0('R^2=',round(cor(plot_data$MTcor, plot_data$GS)^2,4)))
The same should happen with the Log Fold Change
ggplotly(plot_data %>% ggplot(aes(order, log2FoldChange, group=order)) + geom_hline(yintercept=0, color='gray', linetype='dotted') +
geom_boxplot(fill=unique(plot_data$Module)) +
theme_minimal() + xlab('Modules ordered by Module-Diagnosis correlation') + ylab('log2FoldChange'))
ggplotly(plot_data %>% ggplot(aes(MTcor, log2FoldChange)) + geom_hline(yintercept=0, color='gray', linetype='dotted') +
geom_point(color=plot_data$Module, alpha=0.1, aes(id=ID)) + geom_smooth(color='gray', alpha=0.3) +
theme_minimal() + xlab('Module-Diagnosis correlation') + ylab('log2FoldChange') +
ggtitle(paste0('R^2=',round(cor(plot_data$MTcor, plot_data$log2FoldChange)^2,4))))
When studying this plot using samples from all brain regions, we can see a small delation between module-Diagnosis and mean expression that we could explain by what we had observed where overexpressed genes tended to have lower levels of expression than the overexpressed genes, but this patterns is no longer recognisable on this plot.
ggplotly(plot_data %>% ggplot(aes(order, log2(baseMean+1), group=order)) +
geom_hline(yintercept=mean(log2(plot_data$baseMean+1)), color='gray', linetype='dotted') +
geom_boxplot(fill=unique(plot_data$Module)) + theme_minimal() +
xlab('Modules ordered by Module-Diagnosis correlation') + ylab('log2(Mean Expression)'))
plot_data %>% ggplot(aes(MTcor, log2(baseMean+1))) + geom_point(alpha=0.2, color=plot_data$Module, aes(id=ID)) +
geom_hline(yintercept=mean(log2(plot_data$baseMean+1)), color='gray', linetype='dotted') +
geom_smooth(color='gray', alpha=0.3) + theme_minimal() + xlab('Module-Diagnosis correlation') +
ggtitle(paste0('R^2=',round(cor(plot_data$MTcor, log2(plot_data$baseMean+1))^2,4)))
All of the variables seem to agree with each other, Modules with a high correlation with Diagnosis tend to have genes with high values of Log Fold Change as well as high values of Gene Significance, and the gray module, which groups all the genes that weren’t assigned to any cluster tends to have a very poor performance in all of the metrics.
Since SFARI scores genes depending on the strength of the evidence linking it to the development of autism, in theory, there should be some concordance between the metrics we have been studying above and these scores…
There is a big difference between this plot and the one that considers all the brain regions:
SFARI score 6 now has the lowest distribution than the other scores when before it was the highest
SFARI score 1 still has a lower median than the other scores, but not as distinctive as before
SFARI scores 2 to 5 have a higher median than genes with a neuronal-related annotation
In general, this plot shows more concordance between the experimental results and the SFARI genes than the one considering all of the samples
ggplotly(plot_data %>% ggplot(aes(gene.score, abs(GS), fill=gene.score)) + geom_boxplot() +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
ylab('abs(Gene Significance)') + xlab('SFARI Scores') + theme(legend.position='none'))
This plot got inverted completely:
The higher the SFARI score, the higher the Module-Trait correlation
SFARI scores 1 and 2 have significantly higher values of Module-Trait correlation than the rest of the groups
The group with the lowest Module-Diagnosis correlation is SFARI score 6, which is supposed to be the one with the least amount of evidence suggesting a relation to autism
ggplotly(plot_data %>% ggplot(aes(gene.score, abs(MTcor), fill=gene.score)) + geom_boxplot() +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
ylab('abs(Module-Diagnosis Correlation)') + xlab('SFARI Scores') + theme(legend.position='none'))
This time, it seems that the SFARI gene scoring is relatively consistent with the other measurements. These conclusions are opposite to the ones obtained when studying samples from all the brain regions.
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Scientific Linux 7.6 (Nitrogen)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] doParallel_1.0.15 iterators_1.0.12 foreach_1.4.7
## [4] polycor_0.7-10 expss_0.10.1 WGCNA_1.68
## [7] fastcluster_1.1.25 dynamicTreeCut_1.63-1 GGally_1.4.0
## [10] gridExtra_2.3 viridis_0.5.1 viridisLite_0.3.0
## [13] RColorBrewer_1.1-2 dendextend_1.13.3 plotly_4.9.2
## [16] glue_1.3.1 reshape2_1.4.3 forcats_0.4.0
## [19] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3
## [22] readr_1.3.1 tidyr_1.0.2 tibble_2.1.3
## [25] ggplot2_3.2.1 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.5
## [3] Hmisc_4.2-0 plyr_1.8.5
## [5] lazyeval_0.2.2 splines_3.6.0
## [7] BiocParallel_1.20.1 crosstalk_1.0.0
## [9] GenomeInfoDb_1.22.0 robust_0.4-18.2
## [11] digest_0.6.24 htmltools_0.4.0
## [13] GO.db_3.10.0 fansi_0.4.1
## [15] magrittr_1.5 checkmate_1.9.4
## [17] memoise_1.1.0 fit.models_0.5-14
## [19] cluster_2.0.8 annotate_1.64.0
## [21] modelr_0.1.5 matrixStats_0.55.0
## [23] colorspace_1.4-1 blob_1.2.1
## [25] rvest_0.3.5 rrcov_1.4-7
## [27] haven_2.2.0 xfun_0.8
## [29] crayon_1.3.4 RCurl_1.95-4.12
## [31] jsonlite_1.6 genefilter_1.68.0
## [33] impute_1.60.0 survival_2.44-1.1
## [35] gtable_0.3.0 zlibbioc_1.32.0
## [37] XVector_0.26.0 DelayedArray_0.12.2
## [39] BiocGenerics_0.32.0 DEoptimR_1.0-8
## [41] scales_1.1.0 mvtnorm_1.0-11
## [43] DBI_1.1.0 Rcpp_1.0.3
## [45] xtable_1.8-4 htmlTable_1.13.1
## [47] foreign_0.8-71 bit_1.1-15.2
## [49] preprocessCore_1.48.0 Formula_1.2-3
## [51] stats4_3.6.0 htmlwidgets_1.5.1
## [53] httr_1.4.1 ellipsis_0.3.0
## [55] acepack_1.4.1 pkgconfig_2.0.3
## [57] reshape_0.8.8 XML_3.99-0.3
## [59] farver_2.0.3 nnet_7.3-12
## [61] dbplyr_1.4.2 locfit_1.5-9.1
## [63] later_1.0.0 tidyselect_0.2.5
## [65] labeling_0.3 rlang_0.4.4
## [67] AnnotationDbi_1.48.0 munsell_0.5.0
## [69] cellranger_1.1.0 tools_3.6.0
## [71] cli_2.0.1 generics_0.0.2
## [73] RSQLite_2.2.0 broom_0.5.4
## [75] fastmap_1.0.1 evaluate_0.14
## [77] yaml_2.2.0 knitr_1.24
## [79] bit64_0.9-7 fs_1.3.1
## [81] robustbase_0.93-5 nlme_3.1-139
## [83] mime_0.9 xml2_1.2.2
## [85] compiler_3.6.0 rstudioapi_0.10
## [87] reprex_0.3.0 geneplotter_1.64.0
## [89] pcaPP_1.9-73 stringi_1.4.6
## [91] lattice_0.20-38 Matrix_1.2-17
## [93] vctrs_0.2.2 pillar_1.4.3
## [95] lifecycle_0.1.0 data.table_1.12.8
## [97] bitops_1.0-6 httpuv_1.5.2
## [99] GenomicRanges_1.38.0 R6_2.4.1
## [101] latticeExtra_0.6-28 promises_1.1.0
## [103] IRanges_2.20.2 codetools_0.2-16
## [105] MASS_7.3-51.4 assertthat_0.2.1
## [107] SummarizedExperiment_1.16.1 DESeq2_1.26.0
## [109] withr_2.1.2 S4Vectors_0.24.3
## [111] GenomeInfoDbData_1.2.2 mgcv_1.8-28
## [113] hms_0.5.3 grid_3.6.0
## [115] rpart_4.1-15 rmarkdown_1.14
## [117] Cairo_1.5-10 Biobase_2.46.0
## [119] shiny_1.4.0 lubridate_1.7.4
## [121] base64enc_0.1-3